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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Benchmarking GPU workflows 2. Docker and Conda environment management 3. CRISP-DM workflow execution
Topic 2: Data Preparation	17%	- Data Cleaning and Transformation 1. cuDF and pandas data preprocessing 2. Synthetic data generation with RAPIDS 3. Data normalization and standardization
Topic 3: MLOps	19%	- Deployment and Monitoring 1. Memory and capacity evaluation 2. Model deployment in production environments 3. Performance benchmarking and optimization
Topic 4: Machine Learning	15%	- Model Development and Optimization 1. Hyperparameter tuning 2. Memory optimization techniques (mixed precision, batching) 3. Feature engineering 4. Multi-GPU training comparison
Topic 5: Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. GPU-accelerated ETL design and implementation 2. Data caching and performance optimization 3. Distributed data processing frameworks (Dask)
Topic 6: Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Detect anomalies in time series datasets 2. Use cuGraph for graph analytics 3. Perform time series analysis and visualization

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NCP-ADS Exam Questions - NVIDIA-Certified-Professional Accelerated Data Science Torrent Prep & NCP-ADS Test Guide

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q36-Q41):

NEW QUESTION # 36
A financial analyst is working with an irregularly spaced time-series dataset containing cryptocurrency transactions. The timestamps are not evenly distributed, with some periods having dense data and others having sparse entries. The analyst wants to visualize the data efficiently using GPU acceleration.
What is the best preprocessing approach before visualization?

A. Ignore the irregularity and plot the raw timestamps directly without any preprocessing.
B. Convert the dataset to Pandas and use df.resample() to aggregate by fixed time intervals.
C. Sort the data by timestamp and drop all sparse regions using df.dropna().
D. Resample the time-series to a fixed frequency using cuDF.resample() and fill missing values.

Answer: D

NEW QUESTION # 37
You are tasked with profiling a deep learning model using NVIDIA's DLProf to identify performance bottlenecks and optimize resource utilization.
Which of the following statements correctly describes the capabilities of DLProf?

A. DLProf can generate detailed reports that highlight kernel-level execution times and GPU utilization trends.
B. DLProf only works with TensorFlow models and does not support PyTorch-based workloads.
C. DLProf is primarily designed for debugging model accuracy rather than performance analysis.
D. DLProf requires significant modifications to the source code to collect profiling data.

Answer: A

NEW QUESTION # 38
You have trained a machine learning model and are deploying it into production in a cloud-based environment. You need to ensure the model is continuously monitored for performance, including drift in input data and prediction quality.
Which of the following techniques should you use to effectively monitor the model in production? (Select two)

A. Use manual inspection of model predictions periodically to assess accuracy in production.
B. Implement model versioning and rollback procedures to maintain consistency in production models.
C. Set up continuous model retraining pipelines triggered by data drift or performance degradation.
D. Restrict access to the model's API to minimize the number of calls and reduce monitoring overhead.

Answer: B,C

NEW QUESTION # 39
You are processing a dataset with billions of records and want to encode a categorical column efficiently using NVIDIA RAPIDS.
Which of the following methods correctly encodes categorical data using cuDF?

A. df['category_column'] = df['category_column'].apply(lambda x: hash(x) % 1000)
B. df['category_column'] = df['category_column'].one_hot_encode()
C. df['category_column'] = df['category_column'].astype('category')
D. df['category_column'] = LabelEncoder().fit_transform(df['category_column'])

Answer: C

NEW QUESTION # 40
You are working on a predictive maintenance model for industrial equipment. The dataset includes various sensor readings, categorical metadata, and timestamped events.
Which data type is the best choice for a feature representing the operating status of a machine, which has three possible states: "Idle", "Running", and "Error"?

A. Floating-point representation (e.g., 0.1 for Idle, 0.5 for Running, 0.9 for Error).
B. Categorical encoding (one-hot encoding or ordinal encoding).
C. Integer (0, 1, 2) to represent each state numerically.
D. Text (storing states as raw strings like "Idle", "Running", "Error").

Answer: B

NEW QUESTION # 41
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